The unchecked degradation of an individual's alertness is a growing concern and the consequences in some areas are approaching epidemic proportions. As an example, it is estimated that 250,000 drivers per day fall asleep at the wheel. Serious and fatal truck, bus, train and automobile accidents are occurring at an alarming rate. Many injuries and accidents in manufacturing plants are fatigue related. The purpose of monitoring alertness is to prevent these and other emergency situations from happening rather than dealing with them after the fact. For instance, it is already too late to wake someone up after they have fallen asleep at the wheel.
Historically, algorithms for predicting or estimating an individual's alertness were based upon what is often referred to as a two process model. The two process model is made up of a circadian rhythm process and a sleep-wake homeostasis model. The circadian rhythm aspect of the model is typically based solely on a standard time period (e.g., 23-25 hours). The sleep-wake homeostasis model, on the other hand, is typically based solely on actigraphy determinations.
A weakness to the current form of the two process algorithmic model is that it generalizes its prediction of alertness based upon data gathered from a small sample set. In general, the algorithm suffers from a lack of personalization to the individual for which it is intended to be used.